Artificial Intelligence in B2B Sales and Marketing

There’s no denying it: AI is everywhere. On LinkedIn, for example, it pops up in every possible form — from bold promises of revolutionary agents to miracle solutions for the timeless challenges of B2B marketers and sales teams… which, more often than not, turn out to be nothing more than neatly packaged GPT prompts. In short, lots of noise, very little signal.

Our goal is simple: cut through the hype and bring some clarity. At Salesdorado, we stick to our DNA — practical, tested use cases that you can actually apply to your sales, prospecting, or B2B marketing processes.

We’ll help you understand what AI can really change for your growth, sales, or marketing teams — and which tools are worth adding to your stack (and which aren’t).

What can you (really) do with AI in B2B marketing and sales?

The numbers suggest AI is already everywhere: 81% of B2B companies say they use it in their sales or marketing processes, and nearly 80% plan to increase their investments by 2025 (source: Sana). But the reality is more nuanced. Some use cases do make a tangible difference in the field. Yet many teams experiment with tools in a scattered, one-off way — with no real framework or strategy — and end up disappointed.

So the real question isn’t “Should we use AI?” but rather “Where does AI genuinely create value in our B2B processes?”

Automate the dirty work (but not the relationship)

AI shines brightest when it tackles all those repetitive, time-consuming tasks that drain your team’s energy: writing call reports, cleaning up lead databases, pulling data from your CRM, analyzing dozens of campaign replies… In short, the kind of work that doesn’t require human intelligence, but still eats up hours every week.

No wonder this is the top use case identified by decision-makers: 77% point to automating repetitive tasks as their #1 priority (source: Sana). And they’re right. When sales reps stop spending 30% of their time on admin, they can finally focus on what really matters: engaging with prospects, understanding their needs, and building trust.

Personalize at scale — without spamming

Another major contribution of AI is personalization. With the right tools, you can tap into far richer contextual signals: a key recruitment announcement, a shift in company strategy reported in the press, or trends within a specific industry.

That’s a real game changer. Instead of blasting the same pitch to 200 decision-makers, you send 200 different messages – each of which feels more authentic. And mechanically, that boosts your response rates.

But there’s a flip side: many teams go overboard, sending thousands of “personalized” messages that all look like GPT spam. The effect can be worse than a generic pitch. That’s why AI must always be supervised, filtered, and contextualized. Otherwise, you lose credibility instead of gaining it.

Turn overwhelming data into actionable decisions

On the marketing and sales ops side, AI is a lifesaver where humans quickly hit their limits: extracting real insights from oceans of data. Between your CRM, LinkedIn Ads campaigns, outbound emails, recorded calls, and countless customer interactions, the raw material is there. But no one has the time to make sense of it.

A well-trained AI model can process all this data and deliver simple, actionable answers, such as:

• Which leads to follow up with this week.
• Which accounts are showing buying signals.
• Which content to send to which persona to maximize engagement.

Put simply: AI turns dormant data into concrete recommendations.

Boosting marketing creativity (if you stay in control)

When it comes to content, AI works best as an accelerator rather than a creator. Need five variations of a LinkedIn Ads ad? A draft outline for a new playbook? Ten teaser ideas for an email campaign? AI can handle the heavy lifting.

But once again, value only comes when a human stays in the driver’s seat. A marketer with a clear vision gains speed and fresh ideas thanks to AI. A marketer who outsources everything to ChatGPT ends up with bland, generic content — no angle, no personality.

The real advantage lies in the mix: AI to multiply drafts and ideas, humans to refine, set the tone, and add their unique perspective.

The limits of AI

Of course, there are blind spots that need to be acknowledged. Many decision-makers worry about bias and inaccuracies in AI-generated content — and rightly so. A model can hallucinate, misinterpret data, or simply miss the specific context of your market.

On top of that, there are additional challenges:

• Compliance and data protection (GDPR, sensitive data) — identified as a major concern by 74% of B2B companies.
• Internal resistance from teams (over half of respondents mention this as a barrier).
• The risk of over-dependence on tools you don’t fully control.

The key isn’t to reinvent your entire organization overnight, but to test specific use cases, involve your teams, and measure real impact. In other words: you need a pilot in the cockpit.

Plenty of concrete and already mature use cases exist for AI in marketing, CRM, or B2B sales, such as:

• Automatically generating meeting minutes to save sales reps hours of work.
• Analyzing sales calls to detect weak signals and provide continuous coaching.
• Qualifying and enriching leads using public data or business and behavioral signals.
• Personalizing email or LinkedIn campaigns at scale by integrating industry and company context.
• Prioritizing leads in your CRM with predictive models instead of intuition alone.
• Producing multiple variations of marketing content (ads, emails, LinkedIn posts) to accelerate testing and improve conversions.

The real challenge is choosing the right use cases — the ones with the biggest impact in your organization — instead of chasing every shiny new feature.

Three small, concrete steps are always better than a “big AI transformation” that ends up collecting dust. Successful companies move at the pace of adoption, not hype. And crucially, they start with the use cases they want to deploy, then select the right tools — not the other way around.

That’s exactly why we started building curated lists of the best AI tools by use case.

What are the best AI tools (ranked by use case)?

The right way to approach AI isn’t to start with the hottest tool and then scramble to find a use for it. It’s the other way around: start with the use cases you really want to implement, then look for the tool that best fits.

Everyone now knows the big generalist models like ChatGPT, Claude, or Gemini. They’re powerful, versatile, and great for experimentation — but that’s just the tip of the iceberg. In B2B, an entire ecosystem of specialized tools is emerging around specific use cases. Some are impressive, others… not so much.

These tools often rely on general-purpose models (OpenAI, Anthropic, Google, etc.), but add a business layer: sales-oriented interfaces, native CRM integrations, or workflows tailored to prospecting and marketing.

And AI isn’t just about generating content. It also powers:

• Predictive engines.
• Recommendation systems.
• Analytics solutions.

Back in 2022, everyone was dazzled by generative AI with the release of GPT-3. But to reduce AI to text or image generation misses half the picture. In B2B, predictive and analytical models are just as strategic.

At Salesdorado, we’ve started mapping the most common use cases and selecting the best tools for each. The idea is simple: a library of AI tools organized by use case, continuously updated and enriched.

Among the first themes we’ll be covering:

• Generate content (articles, emails, landing pages)
• Automate sales processes (CRM, prospecting, follow-up)
• Accelerate programming / coding
• Create a logo or visual identity
• Generate videos
• Copywriting (emails, scripts, ads)
• Build a chatbot
• Create images
• Write meeting minutes
• Analyze sales calls
• Automatically enrich leads
• Reply to emails automatically
• Create presentations
• Generate LinkedIn posts

We’ll be gradually publishing tool selections for each of these use cases, with hands-on feedback — clearly distinguishing what’s truly useful in B2B from what’s just a gimmick.

The best AI agencies to support your projects

Testing AI tools on your own is one thing. But integrating AI into your marketing, CRM, or sales processes? That’s where the real challenge lies — and it’s not technological, it’s organizational. How do you identify the right use cases, secure your data, convince your teams, and measure impact? That’s where a specialized agency or consultancy can make all the difference.

As with tools, the key is not to fall for agencies that promise to “reinvent everything” with autonomous agents or turnkey solutions. Most of the time, these boil down to pre-packaged prompts wrapped in a glossy interface. The real value lies with partners who can anchor AI in your actual business challenges: prospecting, conversion, retention, and data management.

So, just like with tools, we’ve started mapping the agencies and consultancies that provide concrete support to B2B companies in their AI projects. The goal is to build a clear, actionable comparison that we’ll expand over time.

Some of the key criteria we look at when selecting the best AI agencies:

• Proven experience with B2B projects (not just generic marketing proofs of concept).
• Ability to integrate AI with your existing systems (CRM, marketing automation, customer service).
• A structured approach to change management: training teams, driving gradual adoption, providing long-term support.
• Neutrality toward software vendors — avoiding the bias of pushing a solution out of commercial interest rather than relevance.

We’ve tested these AI tools for you

Reading product sheets or marketing promises is one thing. Knowing whether an AI tool really delivers in a B2B context is another story. At Salesdorado, we don’t just relay vendor claims: we install, configure, and test tools in real-life conditions.

For every tool we test, we share what works, what doesn’t, and — most importantly — in which contexts a tool can be genuinely useful (or not). Many specialized AI tools are impressive when used in the right environment, but fall flat when misused or deployed without a clear use case.

We’ll be gradually publishing detailed feedback on a wide range of AI tools. Each review will include:

• The tool’s scope (what it promises and what it’s designed to do).
• What convinced us.
• What left us wanting more.

In the meantime, remember this: a good tool is only valuable if it aligns with your real use cases. That’s our motto, and you’ll see why. Start with your processes and objectives, and that’s how you’ll uncover the real gems — and that’s where Salesdorado comes in.

Here’s one of the tools we’ve already put to the test:

• Einstein AI (Salesforce AI)